Additional (Potential) Action for Agent in MazeGrid Environment (Reinforcement Learning)
You can definitely define an environment that accepts more types of action, including actions that take multiple steps in a direction.

The first thing you would need to do is implement support for that action in the environment. That is not really a reinforcement learning issue, but like implementing the rules of a board game. You will need to decide things such as what happens if the move would be blocked - does the move succeed up the point of being blocked, does it fail completely, is the reward lower depending on how much the agent tries to overshoot, etc.

After you do that, you will want to write an agent that can choose the new actions. You have a few choices here:


Simplest would be to enumerate all the choices separately and continue to use the same kind of agent as you already have. So instead of $\{U, D, L, R\}$ you might have $\{U1, U2, U3, D1, D2, D3, L1, L2, L3, R1, R2, R3\}$.
If you want to take advantage of generalisation between similar actions (e.g. that action $U3$ is similar to $U2$ and also to $R3$), then you can use some form of coding for the action, such as the relative x,y movement that it is attempting. So you could express $U2$ as $(0,2)$ and $L3$ as $(-3,0)$. For that to then work with Q values, you cannot easily use a table. Instead, you would need to use function approximation, for example a neural network, so you can implement $q(s,a)$ as a parametric function - combining $s,a$ into the input vector, and learn the parameters to that the neural network outputs the correct action value. This is what the Q learning variation DQN can do, as well as other similar RL algorithms that use neural networks.


Using a neural network, instead of tabular Q-learning, is not something you see often with grid world environments. It is a step up in complexity, but it is often required if state space or action space becomes large and might benefit from the generalisation possible from trainable function approximators.